QC plots

These plots indicate common QC properties like distribution of 1) number of reads per cell, 2) number of genes per cell, 3) fraction of reads mapping to mitochondrial genes detected per cell (higher fraction suggests cell disruption), 4) fraction of reads mapping to top 50 expressed genes, 5) and correlation between read depth and number of genes identified per cell.

## [1] "Number of genes  Number of cells"
## [1] 17047  8186

After QC

## [1] "Number of genes  Number of cells"
## [1] 17047  7138

nGene-nUMI correlation plot

PCA Analysis

## [1] "Number of variable genes"
## [1] 1199

PCA plot

List of genes whose expresion shows high correlaton with different top PCs are shown. One should be careful if a PC shows high expression with housekeeping genes (e.g. ribosomal/mitochondiral) experssion.

## [1] "correlation of PC values with nUMI and nGene"
##         nGene     nUMI
## nGene  1.0000  0.95000
## nUMI   0.9500  1.00000
## PC1   -0.0930  0.00270
## PC2   -0.0330 -0.00057
## PC3   -0.0570 -0.00088
## PC4   -0.0400 -0.00210
## PC5    0.0170  0.00450
## PC6    0.0290 -0.00570
## PC7    0.0300  0.00240
## PC8    0.0380  0.01200
## PC9    0.0260  0.00160
## PC10  -0.0210  0.00310
## PC11  -0.0050  0.00110
## PC12   0.0860  0.02700
## PC13  -0.0480 -0.00140
## PC14   0.0220  0.00280
## PC15  -0.0046 -0.00400
## PC16  -0.0470 -0.00062
## PC17   0.0014  0.00610
## PC18   0.0035  0.00099
## PC19   0.0063  0.00520
## PC20  -0.0070 -0.00820

PCA elbow plot

PC Elbow plot can be used to identify most informtive PCs to be used for t-SNE analyses.

tSNE plots

## [1] "Number of cells in different clusters:"
## CD4_TT_E_Memory         Naive_T               3     TT_E_Memory 
##            1888             986             827             795 
##               5   NKT_exhausted               B           Macro 
##             758             733             813             252 
##              11 
##              86
## [1] "Following marker genes were used for cluster labelling"

Expression of markers genes

Genes differentially expressed in different clusters